What Is Azure Model as a Service and How Does It Work

Azure Model as a Service is a cloud-based platform that allows users to deploy, manage, and maintain machine learning models in a scalable and efficient manner. Azure Model as a Service provides a range of benefits, including reduced costs and improved model accuracy.

By leveraging Azure's cloud infrastructure, users can quickly deploy models to production environments and scale them as needed. This allows for faster time-to-market and improved responsiveness to changing business needs.

Azure Model as a Service supports a variety of model types, including deep learning models, decision trees, and clustering models. Users can choose from a range of model types to suit their specific needs and requirements.

Azure Model as a Service also provides a range of tools and services to help users manage and maintain their models, including model monitoring, model tuning, and model deployment.

Check this out: Google Cloud Platform Consultant

Azure Machine Learning Studio offers multiple authoring experiences, including Notebooks, which allow you to write and run your own code in managed Jupyter Notebook servers directly integrated in the studio.

You can also visualize run metrics to analyze and optimize your experiments with visualization. This is especially useful when working with Large Language Models (LLMs) and Generative AI applications.

Azure Machine Learning includes tools to help you build Generative AI applications powered by LLMs, with features like a model catalog, prompt flow, and a suite of tools to streamline the development cycle of AI applications.

Working with LLMs is a powerful way to create innovative AI applications. Azure Machine Learning includes tools to help you build Generative AI applications powered by Large Language Models (LLMs).

You can use Azure Machine Learning Studio and Azure AI Studio to work with LLMs. Azure Machine Learning Studio and Azure AI Studio allow you to work with LLMs.

A model catalog and prompt flow are included in the solution to streamline the development cycle of AI applications. This means you can easily manage and organize your models, and create custom prompts to fine-tune your LLMs.

Both studios provide a suite of tools to help you develop and deploy AI applications quickly and efficiently. This includes everything you need to get started with LLMs and Generative AI.

For your interest: Create an Ai Model in My Azure Tenant

To create a workspace, start by defining it within a resource group in Azure. This centralized place will manage resources required for training a model.

A workspace can group resources based on projects, deployment environments, or organization units. It includes computing targets, data for model training, Notebooks, Experiments, Models, Pipelines, and more.

To access these resources, users need to authenticate using the Azure Active Directory. This ensures secure access to the workspace and its assets.

Here are the essential assets to include in your workspace:

These assets will help you manage and track your model training process effectively.

Enterprise security is a top priority for any business. Azure Model as a Service integrates with Azure cloud platform to add an extra layer of security to your Machine Learning projects.

Azure Virtual Networks with network security groups are used to secure your data. This feature helps protect your data from unauthorized access.

A unique perspective: Azure Data Factory Linked Service

Azure Key Vault is a secure way to store sensitive information, such as access information for storage accounts. This keeps your secrets safe and secure.

Azure Container Registry set up behind a virtual network provides an additional layer of security for your containerized applications.

Here are some key security integrations to consider:

For more information on setting up a secure workspace, check out the tutorial: Tutorial: Set up a secure workspace.

Take a look at this: Azure Service Bus Tutorial

MLOps and DevOps are two related concepts that work together to make machine learning models more efficient and effective. MLOps is a process for developing models for production, ensuring their lifecycle from training to deployment is auditable and reproducible.

DevOps is a set of practices for driving efficiency in developing software at an enterprise scale, which includes team collaboration, process automation, and system integration. MLOps merges with DevOps activities to integrate machine learning into the larger software development framework.

See what others are reading: Azure Devops Service Connections

Machine Learning includes features for monitoring and auditing, such as job artifacts and lineage between jobs and assets. This enables teams to track and audit model performance and deployment.

Some key features enabling MLOps include git integration, MLflow integration, machine learning pipeline scheduling, and Azure Event Grid integration for custom triggers. These features make it easier to manage and deploy machine learning models.

Here are some key benefits of integrating MLOps with DevOps:

By combining MLOps and DevOps, teams can streamline their machine learning workflows, improve collaboration, and ensure that their models are deployed and managed efficiently.

To bring a model into production, you deploy the model, which abstracts the required infrastructure for both batch or real-time model scoring. This allows you to focus on developing and fine-tuning your models without worrying about the underlying infrastructure.

You can deploy a model with a real-time managed endpoint, which enables near real-time scoring via HTTPS. This is ideal for applications that require fast and accurate predictions.

Additional reading: Azure Iaas Examples

To deploy a model, you can use the Azure Machine Learning managed endpoints, which provide a scalable and secure way to run batch or real-time model scoring. This includes using batch endpoints for scoring, which run jobs asynchronously to process data in parallel on compute clusters.

Here are the main deployment options for Azure Machine Learning:

In addition to deployment options, you should also consider the data that is processed for models deployed in Azure Machine Learning. This includes prompts and generated content, as well as uploaded data for fine-tuning models.

Consider reading: Data Modelling Azure

Azure Machine Learning processes two main types of data for models deployed in the platform.

Prompts and generated content are processed, including user-submitted prompts and model-generated output. This output can include content added via retrieval-augmented-generation (RAG), metaprompts, or other application functionality.

Customers can upload their data to the Azure Machine Learning Datastore for fine-tuning models that support it.

Azure ML provides multiple services to help ingest and process big data, including Azure SQL Database, Azure Cosmos DB, and Azure Data Lake.

Take a look at this: Azure Data Services

To deploy a model, you need to bring it into production. You can deploy a model with a real-time managed endpoint, which allows for near real-time scoring, or use batch endpoints for scoring.

Batch scoring involves invoking an endpoint with a reference to data, and the batch endpoint runs jobs asynchronously to process data in parallel on compute clusters. Real-time scoring, on the other hand, involves invoking an endpoint with one or more model deployments and receiving a response in near real time via HTTPS.

To deploy a model, you'll need to choose between deploying to managed compute or serverless APIs. Managed compute deploys model weights to dedicated Virtual Machines and exposes a REST API for real-time inference. Serverless APIs, also known as Models-as-a-Service, provision an API that gives you access to the model hosted and managed by the Azure Machine Learning Service.

Here are some key differences between managed compute and serverless APIs:

You can also create a compute instance to deploy your model. A compute instance is an online computed resource that already has a development environment installed to write and run code in Python. You can select a compute instance with desired CPU, GPU, RAM, and Storage.

For more insights, see: Azure Instance Metadata Service

You can deploy models from the model catalog as serverless APIs for inferencing, giving you access to the model hosted and managed by the Azure Machine Learning Service.

Serverless APIs provide a pay-per-execution model that charges sub-second billing only for the time and resources required to execute the code. This means you only pay for what you use.

Microsoft acts as the data processor for prompts and outputs sent to and generated by a model deployed for pay-as-you-go inferencing (MaaS). Microsoft doesn't share these prompts and outputs with the model provider.

If content filtering (preview) is enabled, prompts and outputs are screened for certain categories of harmful content by the Azure AI Content Safety service in real time.

Microsoft may share customer contact information and transaction details with the model publisher so that they can contact customers regarding the model.

If a model available for serverless API deployment supports fine-tuning, you can upload data to (or designate data already in) an Azure Machine Learning Datastore to fine-tune the model.

Here are the benefits of using fine-tuned models with serverless APIs:

OpenAI's version of the latest 0409 turbo model supports JSON mode and function calling for all inference requests. This means developers have more flexibility when working with OpenAI's models.

Azure OpenAI's version of the latest turbo-2024-04-09 currently doesn't support the use of JSON mode and function calling when making inference requests with image (vision) input. This limitation is worth noting when deciding which platform to use.

Here's a quick summary of the key differences:

To access the GPT-4o and GPT-4o mini models, you need to create or use an existing resource in a supported standard or global standard region where the model is available.

You can deploy the GPT-4o models once your resource is created. If you're performing a programmatic deployment, you'll need to use the following model names: gpt-4oVersion2024-08-06gpt-4o, Version2024-05-13gpt-4o-miniVersion2024-07-18

Intriguing read: Azure Ai Models

OpenAI's latest 0409 turbo model supports JSON mode and function calling for all inference requests, giving developers more flexibility when working with the model.

However, Azure OpenAI's version of the latest turbo-2024-04-09 model has some limitations when it comes to image input requests. Specifically, it doesn't support JSON mode and function calling for image-based inference requests.

But don't worry, text-based input requests are still supported with JSON mode and function calling. This means you can still use these features when working with text inputs, even with Azure OpenAI's version of the turbo model.

On a similar theme: Azure Function Service Bus Trigger

The GPT-4 Turbo model is available for both standard and provisioned deployments.

The provisioned version of this model doesn't support image/vision inference requests, only text input is accepted.

This means you can use the provisioned model for text-based tasks, but if you need to use image or vision inference, you'll need to use the standard model.

Here's a key difference between the two:

Azure Machine Learning ecosystem is a robust platform that integrates various services to support data and analytics.

The Azure ML ecosystem is comprised of multiple services that work together to provide a seamless experience.

We can see the various runs when we click on the experiment name and their respective status.

In our case, the score will give us the prediction about the likelihood of a person getting diabetes.

Inference pipeline is used to make predictions using the model.

The inference pipeline is a crucial part of the Azure ML ecosystem, allowing us to use the model for prediction.

Suggestion: Azure Ml Services

The Azure Machine Learning studio offers multiple authoring experiences depending on the type of project and the level of your past ML experience. You can write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio.

One of the key features of the studio is the ability to visualize run metrics, allowing you to analyze and optimize your experiments. You can also use the Azure Machine Learning designer to train and deploy ML models without writing any code, by dragging and dropping datasets and components to create ML pipelines.

Some of the key capabilities of Azure Machine Learning include on-demand compute, data ingestion engine, workflow orchestration, machine learning model management, and metrics & logs of all model training activities and services. These features make it easier for a wider audience to set up the machine learning workflow and environment manually.

Here are the key features of the Azure Machine Learning studio:

Machine Learning is designed for individuals and teams implementing MLOps within their organization. They can bring ML models into production in a secure and auditable production environment.

Data scientists and ML engineers can use tools to accelerate and automate their day-to-day workflows. This is particularly useful for repetitive tasks that take up a lot of time.

Application developers can use tools for integrating models into applications or services. They can build more sophisticated and accurate applications.

Platform developers can use a robust set of tools, backed by durable Azure Resource Manager APIs, for building advanced ML tooling. This allows for more complex and scalable solutions.

Enterprises working in the Microsoft Azure cloud can use familiar security and role-based access control for infrastructure. This provides an added layer of security and control.

For more insights, see: Azure Access Control Service

Automated featurization and algorithm selection are crucial steps in the machine learning process. In classical ML, data scientists rely on prior experience and intuition to select the right data featurization and algorithm for training, which can be a time-consuming process.

Automated ML (AutoML) speeds up this process, allowing you to use it through the Machine Learning studio UI or the Python SDK.

With AutoML, you can parallel process multiple models, saving time and identifying the best model for a particular use case. This is especially useful in the Azure Machine Learning Studio environment, where optimizing algorithms for the best outcomes is remarkably easy.

The Azure Machine Learning Studio supports only supervised machine learning models, which include classification, regression, and time series forecasting models.

To create a notebook and connect to your workspace in Azure Machine Learning, you'll want to start by creating an Azure Notebook. This is a managed Jupyter Notebook server that's directly integrated into the studio, allowing you to write and run your own code.

You can choose to open your notebooks in VS Code, on the web, or on your desktop, making it easy to work with your code in a format that suits you.

To connect to your workspace, you'll need to import the azureml-core package, a Python package that enables you to connect and write code that uses resources in the workspace.

Here are the steps to create an Azure Notebook and connect to your workspace:

Note that you can also view all the data sources registered in your workspace by going to Home > Datasets > Registered DataSets.